Anomalous phase separation in a correlated electron system: Machine-learning–enabled large-scale kinetic Monte Carlo simulations

Journal Article · 28 April 2022 · Proceedings of the National Academy of Sciences of the United States of America

DOI: https://doi.org/10.1073/pnas.2119957119 · OSTI ID:1866002

Zhang, Sheng  ^[1]; Zhang, Puhan ^[1]; Chern, Gia-Wei ^[1]

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1. Department of Physics, University of Virginia, Charlottesville, VA 22904

Significance Phase separation is crucial to the functionalities of many correlated electron materials with notable examples including colossal magnetoresistance in manganites and high- T _c superconductivity in cuprates. However, the nonequilibrium phase-separation dynamics in such systems are poorly understood theoretically, partly because the required multiscale modeling is computationally very demanding. With the aid of machine-learning methods, we have achieved large-scale dynamical simulations in a representative correlated electron system. We observe an unusual relaxation process that is beyond the framework of classical phase-ordering theories. We also uncover a correlation-induced freezing behavior, which could be a generic feature of phase separation in correlated electron systems.

• Sponsoring Organization: USDOE
• Grant/Contract Number: SC0020330
• OSTI ID: 1866002
• Journal Information: Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Issue: 18 Vol. 119; ISSN 0027-8424
• Publisher: Proceedings of the National Academy of SciencesCopyright Statement
• Country of Publication: United States
• Language: English